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: The PhD position is related to ongoing research within project PID2024-156292OB-I00 "Efficient compression systems for next-generation scientific data", which seeks to improve the performance, scalability
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aims to develop a virtual manufacturing model for compression moulding of high-performance discontinuous fibre composites (DFCs) through novel experimental characterisation method, and validate the model
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that both parameter estimation and model selection can be interpreted as problems of data compression. The principle is simple: if we can compress data, we have learned something about its underlying
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, including part-time or reduced hours of work, opportunities to work from home for many posts, compressed hours and local flexibility in agreeing start and finish times of work are among the extra benefits
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, opportunities to work from home for many posts, compressed hours and local flexibility in agreeing start and finish times of work are among the extra benefits offered by City St George’s, University of London
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, opportunities to work from home for many posts, compressed hours and local flexibility in agreeing start and finish times of work are among the extra benefits offered by St George’s, University of London. Please
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is advertised on a full-time basis, we encourage applications from individuals who would prefer a flexible working pattern including annualised hours, compressed working hours, term-time only and/or
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, opportunities to work from home for many posts, compressed hours and local flexibility in agreeing start and finish times of work are among the extra benefits offered by City St George’s, University of London
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into one ZIP compressed file for submission. Portfolio materials should be from projects completed primarily or completely by applicant. Questions: Charlie Cummings charlie@ufl.edu Application must be
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information theory (Shannon entropy, compressibility, effective complexity and logical depth). The data basis for the analyses are two-fold: first, recent connectome data, i. e. large datasets of all synaptic